Low-end imaging devices such as web-cams and cell phones often record images or videos that are noisy. To improve the quality of images output from such devices, conventional image processing techniques often focus on removing additive white Gaussian noise (AWGN) by filtering images using local neighborhood filters. Linear filters such as arithmetic mean filters and Gaussian filters typically remove noise at the expense of blurring edges in an image. Non-linear filters such as median filters and Wiener filters may be used to reduce blurring, although some a priori knowledge about the noise spectra and the original signal in the image may need to be specified. Noise reduction techniques that do not rely on local neighborhood filtering have also been used to reduce AWGN in images. For example, the non-local means (NLM) method removes noise by averaging pixels in an image, weighted by local patch similarities.
Some image processing techniques for reducing AWGN have been extended to video processing. For example, NLM has been extended to video denoising by aggregating patches in a space-temporal volume. Patches in the space-temporal volume are typically identified using block matching techniques that have been designed for use with video compression.